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import gradio as gr
from ultralytics import YOLOv10
import supervision as sv
MODEL_PATH = 'yolov10n.pt'
model = YOLOv10(MODEL_PATH)
box_annotator = sv.BoxAnnotator()
def detect(image):
results = model(source=image, conf=0.25, verbose=False)[0]
detections = sv.Detections.from_ultralytics(results)
labels = [
f"{model.model.names[class_id]} {confidence:.2f}"
for class_id, confidence in zip(detections.class_id, detections.confidence)
]
annotated_image = box_annotator.annotate(image, detections=detections, labels=labels)
return annotated_image
gradio_app = gr.Blocks()
with gradio_app:
gr.HTML(
"""
<h1 style='text-align: center'>
YOLOv10: Real-Time End-to-End Object Detection
</h1>
""")
gr.HTML(
"""
<h3 style='text-align: center'>
Follow me for more!
<a href='https://twitter.com/kadirnar_ai' target='_blank'>Twitter</a> | <a href='https://github.com/kadirnar' target='_blank'>Github</a> | <a href='https://www.linkedin.com/in/kadir-nar/' target='_blank'>Linkedin</a> | <a href='https://www.huggingface.co/kadirnar/' target='_blank'>HuggingFace</a>
</h3>
""")
input_image = gr.Image(type="numpy")
output_image = gr.Image(type="numpy", label="Annotated Image")
gr.Interface(
fn=detect,
inputs=input_image,
outputs=output_image,
title='YOLOv10 Object Detection',
description='Detect objects in images using the YOLOv10 model'
)
gradio_app.launch() |